Overview

Dataset statistics

Number of variables32
Number of observations1123
Missing cells3026
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory273.2 KiB
Average record size in memory249.1 B

Variable types

Text5
Numeric14
Unsupported7
Categorical3
DateTime2
Boolean1

Alerts

GitHub Repository ID is highly overall correlated with Duration Created to Now in YearsHigh correlation
GitHub Stars is highly overall correlated with GitHub Forks and 7 other fieldsHigh correlation
GitHub Forks is highly overall correlated with GitHub Stars and 7 other fieldsHigh correlation
GitHub Subscribers is highly overall correlated with GitHub Stars and 7 other fieldsHigh correlation
GitHub Open Issues is highly overall correlated with GitHub Stars and 7 other fieldsHigh correlation
GitHub Contributors is highly overall correlated with GitHub Stars and 7 other fieldsHigh correlation
GitHub Network Count is highly overall correlated with GitHub Stars and 7 other fieldsHigh correlation
Repository Size (KB) is highly overall correlated with total lines of GitHub detected code and 1 other fieldsHigh correlation
Duration Created to Now in Years is highly overall correlated with GitHub Repository IDHigh correlation
GitHub Stars (Log Scale) is highly overall correlated with GitHub Stars and 8 other fieldsHigh correlation
total lines of GitHub detected code is highly overall correlated with Repository Size (KB) and 2 other fieldsHigh correlation
total lines of GitHub detected code (Log Scale) is highly overall correlated with Repository Size (KB) and 1 other fieldsHigh correlation
GitHub Forks (Log Scale) is highly overall correlated with GitHub Stars and 8 other fieldsHigh correlation
GitHub Open Issues (Log Scale) is highly overall correlated with GitHub Stars and 7 other fieldsHigh correlation
category is highly overall correlated with GitHub Stars (Log Scale) and 1 other fieldsHigh correlation
Primary language is highly overall correlated with total lines of GitHub detected codeHigh correlation
GitHub Repo Archived is highly imbalanced (90.9%)Imbalance
category is highly imbalanced (75.0%)Imbalance
Project Homepage has 539 (48.0%) missing valuesMissing
GitHub License Type has 551 (49.1%) missing valuesMissing
GitHub Description has 54 (4.8%) missing valuesMissing
GitHub Organization has 729 (64.9%) missing valuesMissing
GitHub Stars (Log Scale) has 44 (3.9%) missing valuesMissing
GitHub Forks (Log Scale) has 435 (38.7%) missing valuesMissing
GitHub Open Issues (Log Scale) has 674 (60.0%) missing valuesMissing
GitHub Stars is highly skewed (γ1 = 21.55540447)Skewed
GitHub Forks is highly skewed (γ1 = 25.30444087)Skewed
GitHub Subscribers is highly skewed (γ1 = 22.78021783)Skewed
GitHub Network Count is highly skewed (γ1 = 25.30419643)Skewed
total lines of GitHub detected code is highly skewed (γ1 = 24.44181837)Skewed
GitHub Repository ID has unique valuesUnique
Project Repo URL has unique valuesUnique
Date Created has unique valuesUnique
Date Most Recent Commit has unique valuesUnique
Project Landscape Category is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Topics is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Detected Languages is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Created to Most Recent Commit is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Created to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Most Recent Commit to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
Negative Duration Most Recent Commit to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Stars has 44 (3.9%) zerosZeros
GitHub Forks has 435 (38.7%) zerosZeros
GitHub Subscribers has 52 (4.6%) zerosZeros
GitHub Open Issues has 674 (60.0%) zerosZeros
GitHub Contributors has 27 (2.4%) zerosZeros
GitHub Network Count has 435 (38.7%) zerosZeros
GitHub Stars (Log Scale) has 316 (28.1%) zerosZeros
GitHub Forks (Log Scale) has 167 (14.9%) zerosZeros
GitHub Open Issues (Log Scale) has 125 (11.1%) zerosZeros

Reproduction

Analysis started2023-10-19 21:29:54.076023
Analysis finished2023-10-19 21:30:09.107936
Duration15.03 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Distinct1090
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:09.314807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length100
Median length65
Mean length15.050757
Min length3

Characters and Unicode

Total characters16902
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1065 ?
Unique (%)94.8%

Sample

1st rowpandas
2nd rownumpy
3rd rowarrow
4th rowduckdb
5th rowparquet-mr
ValueCountFrequency (%)
single-cell-analysis 8
 
0.7%
single-cell-rna-seq-analysis 7
 
0.6%
single_cell_analysis 7
 
0.6%
singlecellanalysis 4
 
0.4%
single-cell-rna-seq 4
 
0.4%
singlecell 3
 
0.3%
orchestratingsinglecellanalysis 3
 
0.3%
single-cell-rna-sequencing-analysis 2
 
0.2%
cytominer 2
 
0.2%
spectre 2
 
0.2%
Other values (1060) 1081
96.3%
2023-10-19T15:30:09.647671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1443
 
8.5%
l 1339
 
7.9%
s 1259
 
7.4%
i 1025
 
6.1%
a 1012
 
6.0%
n 949
 
5.6%
- 847
 
5.0%
c 778
 
4.6%
o 695
 
4.1%
t 617
 
3.7%
Other values (55) 6938
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12372
73.2%
Uppercase Letter 2819
 
16.7%
Dash Punctuation 847
 
5.0%
Connector Punctuation 431
 
2.5%
Decimal Number 413
 
2.4%
Other Punctuation 20
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1443
11.7%
l 1339
10.8%
s 1259
10.2%
i 1025
 
8.3%
a 1012
 
8.2%
n 949
 
7.7%
c 778
 
6.3%
o 695
 
5.6%
t 617
 
5.0%
r 573
 
4.6%
Other values (16) 2682
21.7%
Uppercase Letter
ValueCountFrequency (%)
A 427
15.1%
C 394
14.0%
S 374
13.3%
R 231
 
8.2%
N 205
 
7.3%
M 132
 
4.7%
T 129
 
4.6%
P 128
 
4.5%
I 113
 
4.0%
D 105
 
3.7%
Other values (16) 581
20.6%
Decimal Number
ValueCountFrequency (%)
2 156
37.8%
0 100
24.2%
1 72
17.4%
3 22
 
5.3%
9 21
 
5.1%
8 14
 
3.4%
4 11
 
2.7%
7 8
 
1.9%
6 5
 
1.2%
5 4
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 847
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 431
100.0%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15191
89.9%
Common 1711
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1443
 
9.5%
l 1339
 
8.8%
s 1259
 
8.3%
i 1025
 
6.7%
a 1012
 
6.7%
n 949
 
6.2%
c 778
 
5.1%
o 695
 
4.6%
t 617
 
4.1%
r 573
 
3.8%
Other values (42) 5501
36.2%
Common
ValueCountFrequency (%)
- 847
49.5%
_ 431
25.2%
2 156
 
9.1%
0 100
 
5.8%
1 72
 
4.2%
3 22
 
1.3%
9 21
 
1.2%
. 20
 
1.2%
8 14
 
0.8%
4 11
 
0.6%
Other values (3) 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1443
 
8.5%
l 1339
 
7.9%
s 1259
 
7.4%
i 1025
 
6.1%
a 1012
 
6.0%
n 949
 
5.6%
- 847
 
5.0%
c 778
 
4.6%
o 695
 
4.1%
t 617
 
3.7%
Other values (55) 6938
41.0%

GitHub Repository ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7923865 × 108
Minimum858127
Maximum7.0306429 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:09.748593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum858127
5-th percentile48407428
Q11.4014133 × 108
median2.5217638 × 108
Q33.8810811 × 108
95-th percentile6.0591363 × 108
Maximum7.0306429 × 108
Range7.0220617 × 108
Interquartile range (IQR)2.4796679 × 108

Descriptive statistics

Standard deviation1.7117573 × 108
Coefficient of variation (CV)0.61300875
Kurtosis-0.58841698
Mean2.7923865 × 108
Median Absolute Deviation (MAD)1.2213142 × 108
Skewness0.54510555
Sum3.13585 × 1011
Variance2.9301131 × 1016
MonotonicityNot monotonic
2023-10-19T15:30:09.826014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
858127 1
 
0.1%
292773252 1
 
0.1%
255056675 1
 
0.1%
146028869 1
 
0.1%
232201771 1
 
0.1%
167464808 1
 
0.1%
156759735 1
 
0.1%
255893503 1
 
0.1%
339741407 1
 
0.1%
188461334 1
 
0.1%
Other values (1113) 1113
99.1%
ValueCountFrequency (%)
858127 1
0.1%
908607 1
0.1%
925122 1
0.1%
1571820 1
0.1%
2136580 1
0.1%
2290781 1
0.1%
2425273 1
0.1%
4890816 1
0.1%
5771522 1
0.1%
8678018 1
0.1%
ValueCountFrequency (%)
703064293 1
0.1%
690579583 1
0.1%
689948460 1
0.1%
682462552 1
0.1%
681597755 1
0.1%
679608830 1
0.1%
678475435 1
0.1%
678200716 1
0.1%
677900044 1
0.1%
677883930 1
0.1%

Project Homepage
Text

MISSING 

Distinct206
Distinct (%)35.3%
Missing539
Missing (%)48.0%
Memory size8.9 KiB
2023-10-19T15:30:10.025583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length116
Median length0
Mean length13.777397
Min length0

Characters and Unicode

Total characters8046
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)34.9%

Sample

1st rowhttps://pandas.pydata.org
2nd rowhttps://numpy.org
3rd rowhttps://arrow.apache.org/
4th rowhttp://www.duckdb.org
5th row
ValueCountFrequency (%)
http://nasqar.abudhabi.nyu.edu 2
 
1.0%
https://combine-lab.github.io/salmon 1
 
0.5%
https://www.scrna-tools.org 1
 
0.5%
https://arrow.apache.org 1
 
0.5%
http://www.duckdb.org 1
 
0.5%
https://snakemake.readthedocs.io 1
 
0.5%
http://www.satijalab.org/seurat 1
 
0.5%
https://napari.org 1
 
0.5%
https://scanpy.readthedocs.io 1
 
0.5%
http://scvi-tools.org 1
 
0.5%
Other values (195) 195
94.7%
2023-10-19T15:30:10.337938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 757
 
9.4%
/ 727
 
9.0%
s 489
 
6.1%
i 475
 
5.9%
o 461
 
5.7%
e 413
 
5.1%
. 408
 
5.1%
h 406
 
5.0%
a 366
 
4.5%
p 308
 
3.8%
Other values (55) 3236
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6072
75.5%
Other Punctuation 1339
 
16.6%
Decimal Number 291
 
3.6%
Uppercase Letter 201
 
2.5%
Dash Punctuation 124
 
1.5%
Connector Punctuation 17
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 757
12.5%
s 489
 
8.1%
i 475
 
7.8%
o 461
 
7.6%
e 413
 
6.8%
h 406
 
6.7%
a 366
 
6.0%
p 308
 
5.1%
c 293
 
4.8%
r 283
 
4.7%
Other values (16) 1821
30.0%
Uppercase Letter
ValueCountFrequency (%)
C 29
14.4%
S 27
13.4%
A 23
11.4%
R 16
 
8.0%
M 11
 
5.5%
T 10
 
5.0%
I 9
 
4.5%
N 9
 
4.5%
D 9
 
4.5%
O 9
 
4.5%
Other values (12) 49
24.4%
Decimal Number
ValueCountFrequency (%)
1 63
21.6%
0 58
19.9%
2 42
14.4%
3 23
 
7.9%
4 22
 
7.6%
6 19
 
6.5%
8 17
 
5.8%
7 16
 
5.5%
9 16
 
5.5%
5 15
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 727
54.3%
. 408
30.5%
: 204
 
15.2%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6273
78.0%
Common 1773
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 757
 
12.1%
s 489
 
7.8%
i 475
 
7.6%
o 461
 
7.3%
e 413
 
6.6%
h 406
 
6.5%
a 366
 
5.8%
p 308
 
4.9%
c 293
 
4.7%
r 283
 
4.5%
Other values (38) 2022
32.2%
Common
ValueCountFrequency (%)
/ 727
41.0%
. 408
23.0%
: 204
 
11.5%
- 124
 
7.0%
1 63
 
3.6%
0 58
 
3.3%
2 42
 
2.4%
3 23
 
1.3%
4 22
 
1.2%
6 19
 
1.1%
Other values (7) 83
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 757
 
9.4%
/ 727
 
9.0%
s 489
 
6.1%
i 475
 
5.9%
o 461
 
5.7%
e 413
 
5.1%
. 408
 
5.1%
h 406
 
5.0%
a 366
 
4.5%
p 308
 
3.8%
Other values (55) 3236
40.2%

Project Repo URL
Text

UNIQUE 

Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:10.671194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length157
Median length91
Mean length45.211932
Min length28

Characters and Unicode

Total characters50773
Distinct characters67
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1123 ?
Unique (%)100.0%

Sample

1st rowhttps://github.com/pandas-dev/pandas
2nd rowhttps://github.com/numpy/numpy
3rd rowhttps://github.com/apache/arrow
4th rowhttps://github.com/duckdb/duckdb
5th rowhttps://github.com/apache/parquet-mr
ValueCountFrequency (%)
https://github.com/pandas-dev/pandas 1
 
0.1%
https://github.com/scverse/scanpy 1
 
0.1%
https://github.com/duckdb/duckdb 1
 
0.1%
https://github.com/apache/parquet-mr 1
 
0.1%
https://github.com/snakemake/snakemake 1
 
0.1%
https://github.com/satijalab/seurat 1
 
0.1%
https://github.com/napari/napari 1
 
0.1%
https://github.com/chris-mcginnis-ucsf/doubletfinder 1
 
0.1%
https://github.com/theislab/single-cell-tutorial 1
 
0.1%
https://github.com/sqjin/cellchat 1
 
0.1%
Other values (1113) 1113
99.1%
2023-10-19T15:30:10.992612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4492
 
8.8%
t 4402
 
8.7%
i 3032
 
6.0%
s 2940
 
5.8%
h 2809
 
5.5%
o 2442
 
4.8%
c 2302
 
4.5%
e 2218
 
4.4%
a 2211
 
4.4%
l 1917
 
3.8%
Other values (57) 22008
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37999
74.8%
Other Punctuation 6758
 
13.3%
Uppercase Letter 3741
 
7.4%
Dash Punctuation 1105
 
2.2%
Decimal Number 739
 
1.5%
Connector Punctuation 431
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4402
 
11.6%
i 3032
 
8.0%
s 2940
 
7.7%
h 2809
 
7.4%
o 2442
 
6.4%
c 2302
 
6.1%
e 2218
 
5.8%
a 2211
 
5.8%
l 1917
 
5.0%
g 1817
 
4.8%
Other values (16) 11909
31.3%
Uppercase Letter
ValueCountFrequency (%)
A 476
12.7%
C 466
12.5%
S 451
12.1%
R 255
 
6.8%
N 238
 
6.4%
M 193
 
5.2%
L 173
 
4.6%
T 173
 
4.6%
P 157
 
4.2%
I 157
 
4.2%
Other values (16) 1002
26.8%
Decimal Number
ValueCountFrequency (%)
2 196
26.5%
0 153
20.7%
1 127
17.2%
9 54
 
7.3%
3 47
 
6.4%
8 43
 
5.8%
4 39
 
5.3%
7 31
 
4.2%
5 27
 
3.7%
6 22
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 4492
66.5%
. 1143
 
16.9%
: 1123
 
16.6%
Dash Punctuation
ValueCountFrequency (%)
- 1105
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41740
82.2%
Common 9033
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4402
 
10.5%
i 3032
 
7.3%
s 2940
 
7.0%
h 2809
 
6.7%
o 2442
 
5.9%
c 2302
 
5.5%
e 2218
 
5.3%
a 2211
 
5.3%
l 1917
 
4.6%
g 1817
 
4.4%
Other values (42) 15650
37.5%
Common
ValueCountFrequency (%)
/ 4492
49.7%
. 1143
 
12.7%
: 1123
 
12.4%
- 1105
 
12.2%
_ 431
 
4.8%
2 196
 
2.2%
0 153
 
1.7%
1 127
 
1.4%
9 54
 
0.6%
3 47
 
0.5%
Other values (5) 162
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4492
 
8.8%
t 4402
 
8.7%
i 3032
 
6.0%
s 2940
 
5.8%
h 2809
 
5.5%
o 2442
 
4.8%
c 2302
 
4.5%
e 2218
 
4.4%
a 2211
 
4.4%
l 1917
 
3.8%
Other values (57) 22008
43.3%

Project Landscape Category
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

GitHub Stars
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct160
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.76313
Minimum0
Maximum40031
Zeros44
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:11.090346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q316
95-th percentile193.7
Maximum40031
Range40031
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1505.2762
Coefficient of variation (CV)12.674608
Kurtosis512.24869
Mean118.76313
Median Absolute Deviation (MAD)2
Skewness21.555404
Sum133371
Variance2265856.4
MonotonicityDecreasing
2023-10-19T15:30:11.171848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 316
28.1%
2 130
 
11.6%
3 88
 
7.8%
4 66
 
5.9%
0 44
 
3.9%
5 39
 
3.5%
6 28
 
2.5%
8 24
 
2.1%
7 20
 
1.8%
9 18
 
1.6%
Other values (150) 350
31.2%
ValueCountFrequency (%)
0 44
 
3.9%
1 316
28.1%
2 130
11.6%
3 88
 
7.8%
4 66
 
5.9%
5 39
 
3.5%
6 28
 
2.5%
7 20
 
1.8%
8 24
 
2.1%
9 18
 
1.6%
ValueCountFrequency (%)
40031 1
0.1%
24741 1
0.1%
12614 1
0.1%
12393 1
0.1%
2179 1
0.1%
1955 1
0.1%
1947 1
0.1%
1912 1
0.1%
1612 1
0.1%
1592 1
0.1%

GitHub Forks
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct96
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.96171
Minimum0
Maximum16810
Zeros435
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:11.248926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile52.9
Maximum16810
Range16810
Interquartile range (IQR)6

Descriptive statistics

Standard deviation575.00662
Coefficient of variation (CV)14.758249
Kurtosis691.20536
Mean38.96171
Median Absolute Deviation (MAD)1
Skewness25.304441
Sum43754
Variance330632.61
MonotonicityNot monotonic
2023-10-19T15:30:11.324440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
38.7%
1 167
 
14.9%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
6 30
 
2.7%
5 25
 
2.2%
8 23
 
2.0%
9 19
 
1.7%
7 18
 
1.6%
Other values (86) 194
17.3%
ValueCountFrequency (%)
0 435
38.7%
1 167
 
14.9%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
5 25
 
2.2%
6 30
 
2.7%
7 18
 
1.6%
8 23
 
2.0%
9 19
 
1.7%
ValueCountFrequency (%)
16810 1
0.1%
8647 1
0.1%
3096 1
0.1%
1332 1
0.1%
1157 1
0.1%
851 1
0.1%
536 1
0.1%
474 1
0.1%
473 1
0.1%
422 1
0.1%

GitHub Subscribers
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct45
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2288513
Minimum0
Maximum1121
Zeros52
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:11.399772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile15
Maximum1121
Range1121
Interquartile range (IQR)3

Descriptive statistics

Standard deviation40.027421
Coefficient of variation (CV)6.4261321
Kurtosis583.17272
Mean6.2288513
Median Absolute Deviation (MAD)1
Skewness22.780218
Sum6995
Variance1602.1945
MonotonicityNot monotonic
2023-10-19T15:30:11.472137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 363
32.3%
2 247
22.0%
3 116
 
10.3%
4 74
 
6.6%
0 52
 
4.6%
5 47
 
4.2%
6 37
 
3.3%
7 34
 
3.0%
8 24
 
2.1%
9 19
 
1.7%
Other values (35) 110
 
9.8%
ValueCountFrequency (%)
0 52
 
4.6%
1 363
32.3%
2 247
22.0%
3 116
 
10.3%
4 74
 
6.6%
5 47
 
4.2%
6 37
 
3.3%
7 34
 
3.0%
8 24
 
2.1%
9 19
 
1.7%
ValueCountFrequency (%)
1121 1
0.1%
595 1
0.1%
351 1
0.1%
157 1
0.1%
95 1
0.1%
86 1
0.1%
78 1
0.1%
54 1
0.1%
51 1
0.1%
49 1
0.1%

GitHub Open Issues
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.506679
Minimum0
Maximum3908
Zeros674
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:11.543080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile31.9
Maximum3908
Range3908
Interquartile range (IQR)2

Descriptive statistics

Standard deviation181.0509
Coefficient of variation (CV)9.7830035
Kurtosis354.14214
Mean18.506679
Median Absolute Deviation (MAD)0
Skewness17.999386
Sum20783
Variance32779.428
MonotonicityNot monotonic
2023-10-19T15:30:11.619260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 674
60.0%
1 125
 
11.1%
2 53
 
4.7%
3 33
 
2.9%
6 26
 
2.3%
4 26
 
2.3%
5 14
 
1.2%
8 14
 
1.2%
7 13
 
1.2%
11 8
 
0.7%
Other values (70) 137
 
12.2%
ValueCountFrequency (%)
0 674
60.0%
1 125
 
11.1%
2 53
 
4.7%
3 33
 
2.9%
4 26
 
2.3%
5 14
 
1.2%
6 26
 
2.3%
7 13
 
1.2%
8 14
 
1.2%
9 7
 
0.6%
ValueCountFrequency (%)
3908 1
0.1%
3656 1
0.1%
2193 1
0.1%
1019 1
0.1%
906 1
0.1%
701 1
0.1%
547 1
0.1%
424 1
0.1%
363 1
0.1%
309 1
0.1%

GitHub Contributors
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6073019
Minimum0
Maximum435
Zeros27
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:11.690770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile10
Maximum435
Range435
Interquartile range (IQR)2

Descriptive statistics

Standard deviation25.443233
Coefficient of variation (CV)5.5223717
Kurtosis190.13087
Mean4.6073019
Median Absolute Deviation (MAD)0
Skewness13.186791
Sum5174
Variance647.35813
MonotonicityNot monotonic
2023-10-19T15:30:11.755746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 659
58.7%
2 143
 
12.7%
3 102
 
9.1%
4 60
 
5.3%
5 29
 
2.6%
0 27
 
2.4%
6 15
 
1.3%
7 11
 
1.0%
11 10
 
0.9%
10 10
 
0.9%
Other values (28) 57
 
5.1%
ValueCountFrequency (%)
0 27
 
2.4%
1 659
58.7%
2 143
 
12.7%
3 102
 
9.1%
4 60
 
5.3%
5 29
 
2.6%
6 15
 
1.3%
7 11
 
1.0%
8 9
 
0.8%
9 8
 
0.7%
ValueCountFrequency (%)
435 1
0.1%
411 1
0.1%
367 1
0.1%
280 1
0.1%
253 1
0.1%
190 1
0.1%
150 1
0.1%
125 1
0.1%
83 1
0.1%
79 1
0.1%

GitHub License Type
Categorical

MISSING 

Distinct15
Distinct (%)2.6%
Missing551
Missing (%)49.1%
Memory size8.9 KiB
MIT
211 
GPL-3.0
163 
NOASSERTION
73 
BSD-3-Clause
49 
Apache-2.0
29 
Other values (10)
47 

Length

Max length18
Median length12
Mean length6.7412587
Min length3

Characters and Unicode

Total characters3856
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.7%

Sample

1st rowBSD-3-Clause
2nd rowBSD-3-Clause
3rd rowApache-2.0
4th rowMIT
5th rowApache-2.0

Common Values

ValueCountFrequency (%)
MIT 211
 
18.8%
GPL-3.0 163
 
14.5%
NOASSERTION 73
 
6.5%
BSD-3-Clause 49
 
4.4%
Apache-2.0 29
 
2.6%
CC0-1.0 13
 
1.2%
AGPL-3.0 9
 
0.8%
BSD-2-Clause 7
 
0.6%
GPL-2.0 7
 
0.6%
LGPL-3.0 4
 
0.4%
Other values (5) 7
 
0.6%
(Missing) 551
49.1%

Length

2023-10-19T15:30:11.825451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mit 211
36.9%
gpl-3.0 163
28.5%
noassertion 73
 
12.8%
bsd-3-clause 49
 
8.6%
apache-2.0 29
 
5.1%
cc0-1.0 13
 
2.3%
agpl-3.0 9
 
1.6%
bsd-2-clause 7
 
1.2%
gpl-2.0 7
 
1.2%
lgpl-3.0 4
 
0.7%
Other values (5) 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
- 348
 
9.0%
I 284
 
7.4%
T 284
 
7.4%
0 243
 
6.3%
. 230
 
6.0%
3 226
 
5.9%
M 212
 
5.5%
S 203
 
5.3%
L 188
 
4.9%
P 184
 
4.8%
Other values (26) 1454
37.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2299
59.6%
Decimal Number 530
 
13.7%
Lowercase Letter 449
 
11.6%
Dash Punctuation 348
 
9.0%
Other Punctuation 230
 
6.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 284
12.4%
T 284
12.4%
M 212
9.2%
S 203
8.8%
L 188
8.2%
P 184
8.0%
G 183
8.0%
N 146
6.4%
O 146
6.4%
A 112
 
4.9%
Other values (7) 357
15.5%
Lowercase Letter
ValueCountFrequency (%)
e 89
19.8%
a 87
19.4%
l 59
13.1%
s 59
13.1%
u 57
12.7%
c 31
 
6.9%
h 29
 
6.5%
p 29
 
6.5%
i 3
 
0.7%
r 2
 
0.4%
Other values (2) 4
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 243
45.8%
3 226
42.6%
2 45
 
8.5%
1 13
 
2.5%
4 3
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 348
100.0%
Other Punctuation
ValueCountFrequency (%)
. 230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2748
71.3%
Common 1108
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 284
 
10.3%
T 284
 
10.3%
M 212
 
7.7%
S 203
 
7.4%
L 188
 
6.8%
P 184
 
6.7%
G 183
 
6.7%
N 146
 
5.3%
O 146
 
5.3%
A 112
 
4.1%
Other values (19) 806
29.3%
Common
ValueCountFrequency (%)
- 348
31.4%
0 243
21.9%
. 230
20.8%
3 226
20.4%
2 45
 
4.1%
1 13
 
1.2%
4 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 348
 
9.0%
I 284
 
7.4%
T 284
 
7.4%
0 243
 
6.3%
. 230
 
6.0%
3 226
 
5.9%
M 212
 
5.5%
S 203
 
5.3%
L 188
 
4.9%
P 184
 
4.8%
Other values (26) 1454
37.7%

GitHub Description
Text

MISSING 

Distinct1057
Distinct (%)98.9%
Missing54
Missing (%)4.8%
Memory size8.9 KiB
2023-10-19T15:30:12.019986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10997
Median length343
Mean length134.06642
Min length7

Characters and Unicode

Total characters143317
Distinct characters111
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1045 ?
Unique (%)97.8%

Sample

1st rowFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
2nd rowThe fundamental package for scientific computing with Python.
3rd rowApache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
4th rowDuckDB is an in-process SQL OLAP Database Management System
5th rowApache Parquet
ValueCountFrequency (%)
analysis 950
 
4.7%
of 791
 
3.9%
and 673
 
3.3%
the 669
 
3.3%
for 654
 
3.3%
cell 517
 
2.6%
single-cell 468
 
2.3%
data 451
 
2.2%
single 396
 
2.0%
a 366
 
1.8%
Other values (4025) 14177
70.5%
2023-10-19T15:30:12.329464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19189
13.4%
e 12687
 
8.9%
a 9933
 
6.9%
i 9819
 
6.9%
n 8845
 
6.2%
s 8728
 
6.1%
l 8228
 
5.7%
o 7918
 
5.5%
t 7893
 
5.5%
r 6159
 
4.3%
Other values (101) 43918
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111625
77.9%
Space Separator 19228
 
13.4%
Uppercase Letter 7159
 
5.0%
Other Punctuation 2195
 
1.5%
Dash Punctuation 1305
 
0.9%
Decimal Number 1194
 
0.8%
Close Punctuation 235
 
0.2%
Open Punctuation 229
 
0.2%
Math Symbol 51
 
< 0.1%
Final Punctuation 40
 
< 0.1%
Other values (6) 56
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12687
11.4%
a 9933
 
8.9%
i 9819
 
8.8%
n 8845
 
7.9%
s 8728
 
7.8%
l 8228
 
7.4%
o 7918
 
7.1%
t 7893
 
7.1%
r 6159
 
5.5%
c 4959
 
4.4%
Other values (19) 26456
23.7%
Uppercase Letter
ValueCountFrequency (%)
A 1116
15.6%
S 777
10.9%
R 737
10.3%
C 708
9.9%
N 606
8.5%
T 405
 
5.7%
D 359
 
5.0%
P 329
 
4.6%
I 320
 
4.5%
M 289
 
4.0%
Other values (16) 1513
21.1%
Other Punctuation
ValueCountFrequency (%)
. 909
41.4%
, 624
28.4%
: 186
 
8.5%
/ 173
 
7.9%
" 169
 
7.7%
% 40
 
1.8%
' 37
 
1.7%
; 18
 
0.8%
& 14
 
0.6%
! 7
 
0.3%
Other values (4) 18
 
0.8%
Other Symbol
ValueCountFrequency (%)
6
37.5%
🐟 1
 
6.2%
🏔 1
 
6.2%
🌍 1
 
6.2%
🍱 1
 
6.2%
🍣 1
 
6.2%
🦀 1
 
6.2%
🔬 1
 
6.2%
🧬 1
 
6.2%
🌸 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 262
21.9%
0 252
21.1%
1 207
17.3%
3 92
 
7.7%
9 86
 
7.2%
5 70
 
5.9%
7 70
 
5.9%
4 62
 
5.2%
8 48
 
4.0%
6 45
 
3.8%
Math Symbol
ValueCountFrequency (%)
= 22
43.1%
+ 15
29.4%
> 9
17.6%
< 4
 
7.8%
~ 1
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 1302
99.8%
2
 
0.2%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
19189
99.8%
  39
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 232
98.7%
] 3
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 226
98.7%
[ 3
 
1.3%
Final Punctuation
ValueCountFrequency (%)
32
80.0%
8
 
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Initial Punctuation
ValueCountFrequency (%)
8
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Other Letter
ValueCountFrequency (%)
1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118783
82.9%
Common 24531
 
17.1%
Inherited 1
 
< 0.1%
Han 1
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
19189
78.2%
- 1302
 
5.3%
. 909
 
3.7%
, 624
 
2.5%
2 262
 
1.1%
0 252
 
1.0%
) 232
 
0.9%
( 226
 
0.9%
1 207
 
0.8%
: 186
 
0.8%
Other values (44) 1142
 
4.7%
Latin
ValueCountFrequency (%)
e 12687
 
10.7%
a 9933
 
8.4%
i 9819
 
8.3%
n 8845
 
7.4%
s 8728
 
7.3%
l 8228
 
6.9%
o 7918
 
6.7%
t 7893
 
6.6%
r 6159
 
5.2%
c 4959
 
4.2%
Other values (44) 33614
28.3%
Inherited
ValueCountFrequency (%)
1
100.0%
Han
ValueCountFrequency (%)
1
100.0%
Greek
ValueCountFrequency (%)
α 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143204
99.9%
None 54
 
< 0.1%
Punctuation 51
 
< 0.1%
Geometric Shapes 6
 
< 0.1%
VS 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19189
13.4%
e 12687
 
8.9%
a 9933
 
6.9%
i 9819
 
6.9%
n 8845
 
6.2%
s 8728
 
6.1%
l 8228
 
5.7%
o 7918
 
5.5%
t 7893
 
5.5%
r 6159
 
4.3%
Other values (79) 43805
30.6%
None
ValueCountFrequency (%)
  39
72.2%
ü 3
 
5.6%
🐟 1
 
1.9%
🏔 1
 
1.9%
🌍 1
 
1.9%
🍱 1
 
1.9%
🍣 1
 
1.9%
🦀 1
 
1.9%
é 1
 
1.9%
🔬 1
 
1.9%
Other values (4) 4
 
7.4%
Punctuation
ValueCountFrequency (%)
32
62.7%
8
 
15.7%
8
 
15.7%
2
 
3.9%
1
 
2.0%
Geometric Shapes
ValueCountFrequency (%)
6
100.0%
VS
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

GitHub Topics
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

GitHub Organization
Text

MISSING 

Distinct263
Distinct (%)66.8%
Missing729
Missing (%)64.9%
Memory size8.9 KiB
2023-10-19T15:30:12.576788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length37
Median length28
Mean length11.530457
Min length3

Characters and Unicode

Total characters4543
Distinct characters57
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique203 ?
Unique (%)51.5%

Sample

1st rowpandas-dev
2nd rownumpy
3rd rowapache
4th rowduckdb
5th rowapache
ValueCountFrequency (%)
theislab 13
 
3.3%
ucdavis-bioinformatics-training 9
 
2.3%
teichlab 8
 
2.0%
broadinstitute 7
 
1.8%
scverse 6
 
1.5%
immunogenomics 5
 
1.3%
cytomining 5
 
1.3%
kharchenkolab 5
 
1.3%
icbi-lab 5
 
1.3%
oshlack 5
 
1.3%
Other values (253) 326
82.7%
2023-10-19T15:30:12.886825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 481
 
10.6%
i 361
 
7.9%
e 331
 
7.3%
b 292
 
6.4%
l 286
 
6.3%
n 280
 
6.2%
o 260
 
5.7%
s 252
 
5.5%
r 213
 
4.7%
c 198
 
4.4%
Other values (47) 1589
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3910
86.1%
Uppercase Letter 458
 
10.1%
Dash Punctuation 162
 
3.6%
Decimal Number 13
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 481
12.3%
i 361
 
9.2%
e 331
 
8.5%
b 292
 
7.5%
l 286
 
7.3%
n 280
 
7.2%
o 260
 
6.6%
s 252
 
6.4%
r 213
 
5.4%
c 198
 
5.1%
Other values (16) 956
24.5%
Uppercase Letter
ValueCountFrequency (%)
L 70
15.3%
C 48
10.5%
B 47
 
10.3%
S 37
 
8.1%
M 31
 
6.8%
I 30
 
6.6%
T 21
 
4.6%
G 20
 
4.4%
R 18
 
3.9%
A 18
 
3.9%
Other values (14) 118
25.8%
Decimal Number
ValueCountFrequency (%)
2 3
23.1%
0 3
23.1%
1 3
23.1%
4 2
15.4%
9 1
 
7.7%
3 1
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4368
96.1%
Common 175
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 481
 
11.0%
i 361
 
8.3%
e 331
 
7.6%
b 292
 
6.7%
l 286
 
6.5%
n 280
 
6.4%
o 260
 
6.0%
s 252
 
5.8%
r 213
 
4.9%
c 198
 
4.5%
Other values (40) 1414
32.4%
Common
ValueCountFrequency (%)
- 162
92.6%
2 3
 
1.7%
0 3
 
1.7%
1 3
 
1.7%
4 2
 
1.1%
9 1
 
0.6%
3 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 481
 
10.6%
i 361
 
7.9%
e 331
 
7.3%
b 292
 
6.4%
l 286
 
6.3%
n 280
 
6.2%
o 260
 
5.7%
s 252
 
5.5%
r 213
 
4.7%
c 198
 
4.4%
Other values (47) 1589
35.0%

GitHub Network Count
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct97
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.024043
Minimum0
Maximum16810
Zeros435
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:12.980227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile54.8
Maximum16810
Range16810
Interquartile range (IQR)6

Descriptive statistics

Standard deviation575.00601
Coefficient of variation (CV)14.73466
Kurtosis691.19731
Mean39.024043
Median Absolute Deviation (MAD)1
Skewness25.304196
Sum43824
Variance330631.91
MonotonicityNot monotonic
2023-10-19T15:30:13.053906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
38.7%
1 166
 
14.8%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
6 29
 
2.6%
5 25
 
2.2%
8 23
 
2.0%
9 20
 
1.8%
7 18
 
1.6%
Other values (87) 195
17.4%
ValueCountFrequency (%)
0 435
38.7%
1 166
 
14.8%
2 103
 
9.2%
3 64
 
5.7%
4 45
 
4.0%
5 25
 
2.2%
6 29
 
2.6%
7 18
 
1.6%
8 23
 
2.0%
9 20
 
1.8%
ValueCountFrequency (%)
16810 1
0.1%
8647 1
0.1%
3096 1
0.1%
1332 1
0.1%
1157 1
0.1%
851 1
0.1%
536 1
0.1%
474 1
0.1%
473 1
0.1%
422 1
0.1%

GitHub Detected Languages
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Date Created
Date

UNIQUE 

Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2010-08-24 01:37:33+00:00
Maximum2023-10-10 14:24:36+00:00
2023-10-19T15:30:13.130678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:13.203305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2011-06-21 13:44:00+00:00
Maximum2023-10-16 17:19:16+00:00
2023-10-19T15:30:13.274488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:13.354556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Duration Created to Most Recent Commit
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Duration Created to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Duration Most Recent Commit to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Repository Size (KB)
Real number (ℝ)

HIGH CORRELATION 

Distinct1010
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75421.258
Minimum1
Maximum1922901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:13.433550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.1
Q1688
median10748
Q365047
95-th percentile360260.2
Maximum1922901
Range1922900
Interquartile range (IQR)64359

Descriptive statistics

Standard deviation186112.08
Coefficient of variation (CV)2.4676342
Kurtosis39.984656
Mean75421.258
Median Absolute Deviation (MAD)10707
Skewness5.5453982
Sum84698073
Variance3.4637706 × 1010
MonotonicityNot monotonic
2023-10-19T15:30:13.612358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 10
 
0.9%
26 7
 
0.6%
13 6
 
0.5%
3 6
 
0.5%
67 5
 
0.4%
16 5
 
0.4%
24 5
 
0.4%
22 4
 
0.4%
30 4
 
0.4%
9 4
 
0.4%
Other values (1000) 1067
95.0%
ValueCountFrequency (%)
1 2
 
0.2%
2 2
 
0.2%
3 6
0.5%
4 3
 
0.3%
5 3
 
0.3%
6 10
0.9%
7 3
 
0.3%
8 3
 
0.3%
9 4
 
0.4%
10 1
 
0.1%
ValueCountFrequency (%)
1922901 1
0.1%
1921765 1
0.1%
1792729 1
0.1%
1590023 1
0.1%
1496132 1
0.1%
1461986 1
0.1%
1447466 1
0.1%
1293566 1
0.1%
1245051 1
0.1%
1068214 1
0.1%

GitHub Repo Archived
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1110 
True
 
13
ValueCountFrequency (%)
False 1110
98.8%
True 13
 
1.2%
2023-10-19T15:30:13.678247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Duration Created to Now in Years
Real number (ℝ)

HIGH CORRELATION 

Distinct922
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8459947
Minimum0.016438356
Maximum13.153425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:13.738535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.016438356
5-th percentile0.64136986
Q12.2383562
median3.5424658
Q35.2767123
95-th percentile7.8227397
Maximum13.153425
Range13.136986
Interquartile range (IQR)3.0383562

Descriptive statistics

Standard deviation2.2608595
Coefficient of variation (CV)0.5878478
Kurtosis0.82630623
Mean3.8459947
Median Absolute Deviation (MAD)1.4767123
Skewness0.76392392
Sum4319.0521
Variance5.1114857
MonotonicityNot monotonic
2023-10-19T15:30:13.817612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.142465753 4
 
0.4%
2.695890411 4
 
0.4%
0.7369863014 3
 
0.3%
0.8356164384 3
 
0.3%
4.383561644 3
 
0.3%
2.679452055 3
 
0.3%
3.682191781 3
 
0.3%
3.504109589 3
 
0.3%
5.520547945 3
 
0.3%
2.252054795 3
 
0.3%
Other values (912) 1091
97.2%
ValueCountFrequency (%)
0.01643835616 1
0.1%
0.09315068493 1
0.1%
0.09589041096 1
0.1%
0.1452054795 1
0.1%
0.1506849315 1
0.1%
0.1643835616 1
0.1%
0.1726027397 2
0.2%
0.1753424658 2
0.2%
0.2219178082 2
0.2%
0.2328767123 1
0.1%
ValueCountFrequency (%)
13.15342466 1
0.1%
13.09589041 1
0.1%
13.07945205 1
0.1%
12.53972603 1
0.1%
12.21643836 1
0.1%
12.1369863 1
0.1%
12.07671233 1
0.1%
11.28767123 1
0.1%
11.09863014 1
0.1%
10.60821918 1
0.1%

Negative Duration Most Recent Commit to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

category
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
github-query-result
1004 
adjacent-tools
 
100
relevant-open-source
 
8
microscopy-analysis-tools
 
7
loi-focus
 
4

Length

Max length25
Median length19
Mean length18.563669
Min length9

Characters and Unicode

Total characters20847
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrelevant-open-source
2nd rowrelevant-open-source
3rd rowrelevant-open-source
4th rowrelevant-open-source
5th rowrelevant-open-source

Common Values

ValueCountFrequency (%)
github-query-result 1004
89.4%
adjacent-tools 100
 
8.9%
relevant-open-source 8
 
0.7%
microscopy-analysis-tools 7
 
0.6%
loi-focus 4
 
0.4%

Length

2023-10-19T15:30:13.893463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T15:30:13.957427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
github-query-result 1004
89.4%
adjacent-tools 100
 
8.9%
relevant-open-source 8
 
0.7%
microscopy-analysis-tools 7
 
0.6%
loi-focus 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
u 3024
14.5%
t 2223
10.7%
- 2142
10.3%
e 2140
10.3%
r 2031
9.7%
s 1144
 
5.5%
l 1130
 
5.4%
i 1022
 
4.9%
y 1018
 
4.9%
g 1004
 
4.8%
Other values (13) 3969
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18705
89.7%
Dash Punctuation 2142
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 3024
16.2%
t 2223
11.9%
e 2140
11.4%
r 2031
10.9%
s 1144
 
6.1%
l 1130
 
6.0%
i 1022
 
5.5%
y 1018
 
5.4%
g 1004
 
5.4%
q 1004
 
5.4%
Other values (12) 2965
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 2142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18705
89.7%
Common 2142
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 3024
16.2%
t 2223
11.9%
e 2140
11.4%
r 2031
10.9%
s 1144
 
6.1%
l 1130
 
6.0%
i 1022
 
5.5%
y 1018
 
5.4%
g 1004
 
5.4%
q 1004
 
5.4%
Other values (12) 2965
15.9%
Common
ValueCountFrequency (%)
- 2142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 3024
14.5%
t 2223
10.7%
- 2142
10.3%
e 2140
10.3%
r 2031
9.7%
s 1144
 
5.5%
l 1130
 
5.4%
i 1022
 
4.9%
y 1018
 
4.9%
g 1004
 
4.8%
Other values (13) 3969
19.0%

GitHub Stars (Log Scale)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct159
Distinct (%)14.7%
Missing44
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1.7550269
Minimum0
Maximum10.597409
Zeros316
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:14.028064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.3862944
Q32.8903718
95-th percentile5.3126267
Maximum10.597409
Range10.597409
Interquartile range (IQR)2.8903718

Descriptive statistics

Standard deviation1.7911075
Coefficient of variation (CV)1.0205585
Kurtosis1.3162256
Mean1.7550269
Median Absolute Deviation (MAD)1.3862944
Skewness1.1530398
Sum1893.674
Variance3.2080662
MonotonicityDecreasing
2023-10-19T15:30:14.104770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 316
28.1%
0.6931471806 130
 
11.6%
1.098612289 88
 
7.8%
1.386294361 66
 
5.9%
1.609437912 39
 
3.5%
1.791759469 28
 
2.5%
2.079441542 24
 
2.1%
1.945910149 20
 
1.8%
2.197224577 18
 
1.6%
2.708050201 15
 
1.3%
Other values (149) 335
29.8%
(Missing) 44
 
3.9%
ValueCountFrequency (%)
0 316
28.1%
0.6931471806 130
11.6%
1.098612289 88
 
7.8%
1.386294361 66
 
5.9%
1.609437912 39
 
3.5%
1.791759469 28
 
2.5%
1.945910149 20
 
1.8%
2.079441542 24
 
2.1%
2.197224577 18
 
1.6%
2.302585093 10
 
0.9%
ValueCountFrequency (%)
10.59740943 1
0.1%
10.11621707 1
0.1%
9.442562587 1
0.1%
9.424887076 1
0.1%
7.686621335 1
0.1%
7.578145472 1
0.1%
7.574045005 1
0.1%
7.555905094 1
0.1%
7.385230923 1
0.1%
7.372746366 1
0.1%

total lines of GitHub detected code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1116
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9334464.7
Minimum101
Maximum2.2291975 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:14.177869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile6011.5
Q146763
median195359
Q31562065
95-th percentile32865361
Maximum2.2291975 × 109
Range2.2291974 × 109
Interquartile range (IQR)1515302

Descriptive statistics

Standard deviation75331005
Coefficient of variation (CV)8.0702008
Kurtosis689.29143
Mean9334464.7
Median Absolute Deviation (MAD)182408
Skewness24.441818
Sum1.0482604 × 1010
Variance5.6747603 × 1015
MonotonicityNot monotonic
2023-10-19T15:30:14.250524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
954832 4
 
0.4%
50043 2
 
0.2%
102304 2
 
0.2%
7465 2
 
0.2%
3354 2
 
0.2%
22492093 1
 
0.1%
2459764 1
 
0.1%
60348 1
 
0.1%
41456 1
 
0.1%
60940 1
 
0.1%
Other values (1106) 1106
98.5%
ValueCountFrequency (%)
101 1
0.1%
397 1
0.1%
508 1
0.1%
547 1
0.1%
587 1
0.1%
695 1
0.1%
968 1
0.1%
1093 1
0.1%
1120 1
0.1%
1203 1
0.1%
ValueCountFrequency (%)
2229197511 1
0.1%
880847605 1
0.1%
280960563 1
0.1%
277061560 1
0.1%
232185079 1
0.1%
192917347 1
0.1%
183885622 1
0.1%
182341397 1
0.1%
178105096 1
0.1%
166604099 1
0.1%

total lines of GitHub detected code (Log Scale)
Real number (ℝ)

HIGH CORRELATION 

Distinct1116
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.554047
Minimum4.6151205
Maximum21.524907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:14.320919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4.6151205
5-th percentile8.7014293
Q110.752846
median12.182594
Q314.261488
95-th percentile17.307912
Maximum21.524907
Range16.909787
Interquartile range (IQR)3.5086417

Descriptive statistics

Standard deviation2.6321878
Coefficient of variation (CV)0.20966846
Kurtosis-0.19078755
Mean12.554047
Median Absolute Deviation (MAD)1.6178114
Skewness0.38674064
Sum14098.195
Variance6.9284124
MonotonicityNot monotonic
2023-10-19T15:30:14.396360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.76929069 4
 
0.4%
10.82063791 2
 
0.2%
11.53570405 2
 
0.2%
8.91798071 2
 
0.2%
8.117908942 2
 
0.2%
16.92867438 1
 
0.1%
14.71557597 1
 
0.1%
11.00788309 1
 
0.1%
10.6323879 1
 
0.1%
11.01764505 1
 
0.1%
Other values (1106) 1106
98.5%
ValueCountFrequency (%)
4.615120517 1
0.1%
5.983936281 1
0.1%
6.230481448 1
0.1%
6.304448802 1
0.1%
6.37502482 1
0.1%
6.543911846 1
0.1%
6.875232087 1
0.1%
6.996681488 1
0.1%
7.021083964 1
0.1%
7.092573716 1
0.1%
ValueCountFrequency (%)
21.5249075 1
0.1%
20.59639519 1
0.1%
19.45372487 1
0.1%
19.43975028 1
0.1%
19.26304537 1
0.1%
19.0777724 1
0.1%
19.0298245 1
0.1%
19.0213913 1
0.1%
18.99788436 1
0.1%
18.93113089 1
0.1%

GitHub Forks (Log Scale)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct95
Distinct (%)13.8%
Missing435
Missing (%)38.7%
Infinite0
Infinite (%)0.0%
Mean1.6249093
Minimum0
Maximum9.7297292
Zeros167
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:14.473212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.69314718
median1.3862944
Q32.4849066
95-th percentile4.4343175
Maximum9.7297292
Range9.7297292
Interquartile range (IQR)1.7917595

Descriptive statistics

Standard deviation1.5268047
Coefficient of variation (CV)0.93962453
Kurtosis2.3275895
Mean1.6249093
Median Absolute Deviation (MAD)1.0116009
Skewness1.254457
Sum1117.9376
Variance2.3311325
MonotonicityNot monotonic
2023-10-19T15:30:14.549847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167
 
14.9%
0.6931471806 103
 
9.2%
1.098612289 64
 
5.7%
1.386294361 45
 
4.0%
1.791759469 30
 
2.7%
1.609437912 25
 
2.2%
2.079441542 23
 
2.0%
2.197224577 19
 
1.7%
1.945910149 18
 
1.6%
2.564949357 13
 
1.2%
Other values (85) 181
16.1%
(Missing) 435
38.7%
ValueCountFrequency (%)
0 167
14.9%
0.6931471806 103
9.2%
1.098612289 64
 
5.7%
1.386294361 45
 
4.0%
1.609437912 25
 
2.2%
1.791759469 30
 
2.7%
1.945910149 18
 
1.6%
2.079441542 23
 
2.0%
2.197224577 19
 
1.7%
2.302585093 10
 
0.9%
ValueCountFrequency (%)
9.729729226 1
0.1%
9.064967719 1
0.1%
8.037866235 1
0.1%
7.194436851 1
0.1%
7.053585727 1
0.1%
6.746412129 1
0.1%
6.284134161 1
0.1%
6.161207322 1
0.1%
6.159095388 1
0.1%
6.045005314 1
0.1%

GitHub Open Issues (Log Scale)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct79
Distinct (%)17.6%
Missing674
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1.675776
Minimum0
Maximum8.270781
Zeros125
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-19T15:30:14.622969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.3862944
Q32.7080502
95-th percentile4.5869563
Maximum8.270781
Range8.270781
Interquartile range (IQR)2.7080502

Descriptive statistics

Standard deviation1.6126548
Coefficient of variation (CV)0.96233313
Kurtosis1.2677127
Mean1.675776
Median Absolute Deviation (MAD)1.3862944
Skewness1.0876703
Sum752.42343
Variance2.6006554
MonotonicityNot monotonic
2023-10-19T15:30:14.695117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
 
11.1%
0.6931471806 53
 
4.7%
1.098612289 33
 
2.9%
1.791759469 26
 
2.3%
1.386294361 26
 
2.3%
1.609437912 14
 
1.2%
2.079441542 14
 
1.2%
1.945910149 13
 
1.2%
2.397895273 8
 
0.7%
2.944438979 8
 
0.7%
Other values (69) 129
 
11.5%
(Missing) 674
60.0%
ValueCountFrequency (%)
0 125
11.1%
0.6931471806 53
4.7%
1.098612289 33
 
2.9%
1.386294361 26
 
2.3%
1.609437912 14
 
1.2%
1.791759469 26
 
2.3%
1.945910149 13
 
1.2%
2.079441542 14
 
1.2%
2.197224577 7
 
0.6%
2.302585093 6
 
0.5%
ValueCountFrequency (%)
8.270781013 1
0.1%
8.204124933 1
0.1%
7.693025748 1
0.1%
6.926577033 1
0.1%
6.809039306 1
0.1%
6.552507887 1
0.1%
6.304448802 1
0.1%
6.049733455 1
0.1%
5.894402834 1
0.1%
5.733341277 1
0.1%

Primary language
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
R
432 
Python
218 
Jupyter Notebook
195 
HTML
113 
MATLAB
49 
Other values (28)
116 

Length

Max length24
Median length17
Mean length5.5556545
Min length1

Characters and Unicode

Total characters6239
Distinct characters50
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)1.4%

Sample

1st rowPython
2nd rowPython
3rd rowC++
4th rowC++
5th rowJava

Common Values

ValueCountFrequency (%)
R 432
38.5%
Python 218
19.4%
Jupyter Notebook 195
17.4%
HTML 113
 
10.1%
MATLAB 49
 
4.4%
Shell 16
 
1.4%
C++ 15
 
1.3%
Java 15
 
1.3%
JavaScript 9
 
0.8%
Julia 9
 
0.8%
Other values (23) 52
 
4.6%

Length

2023-10-19T15:30:14.762484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 432
32.6%
python 218
16.4%
jupyter 195
14.7%
notebook 195
14.7%
html 113
 
8.5%
matlab 49
 
3.7%
c 22
 
1.7%
shell 16
 
1.2%
java 15
 
1.1%
javascript 9
 
0.7%
Other values (29) 63
 
4.7%

Most occurring characters

ValueCountFrequency (%)
o 826
 
13.2%
t 632
 
10.1%
e 441
 
7.1%
R 434
 
7.0%
y 414
 
6.6%
h 235
 
3.8%
J 233
 
3.7%
P 226
 
3.6%
n 225
 
3.6%
r 220
 
3.5%
Other values (40) 2353
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4054
65.0%
Uppercase Letter 1949
31.2%
Space Separator 204
 
3.3%
Math Symbol 30
 
0.5%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 826
20.4%
t 632
15.6%
e 441
10.9%
y 414
10.2%
h 235
 
5.8%
n 225
 
5.6%
r 220
 
5.4%
u 209
 
5.2%
p 208
 
5.1%
k 199
 
4.9%
Other values (14) 445
11.0%
Uppercase Letter
ValueCountFrequency (%)
R 434
22.3%
J 233
12.0%
P 226
11.6%
N 202
10.4%
T 170
 
8.7%
M 167
 
8.6%
L 164
 
8.4%
H 114
 
5.8%
A 100
 
5.1%
B 51
 
2.6%
Other values (12) 88
 
4.5%
Space Separator
ValueCountFrequency (%)
204
100.0%
Math Symbol
ValueCountFrequency (%)
+ 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6003
96.2%
Common 236
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 826
 
13.8%
t 632
 
10.5%
e 441
 
7.3%
R 434
 
7.2%
y 414
 
6.9%
h 235
 
3.9%
J 233
 
3.9%
P 226
 
3.8%
n 225
 
3.7%
r 220
 
3.7%
Other values (36) 2117
35.3%
Common
ValueCountFrequency (%)
204
86.4%
+ 30
 
12.7%
- 1
 
0.4%
. 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 826
 
13.2%
t 632
 
10.1%
e 441
 
7.1%
R 434
 
7.0%
y 414
 
6.6%
h 235
 
3.8%
J 233
 
3.7%
P 226
 
3.6%
n 225
 
3.6%
r 220
 
3.5%
Other values (40) 2353
37.7%

Interactions

2023-10-19T15:30:07.828174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.200024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.152870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.945416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.720971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.556914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.357740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.099626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.867609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.823599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.672964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.413057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.157407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.084551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.877905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.293593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.210786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.003265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.775698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.617425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.413393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.157223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.927124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.924790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.729456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.467799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.215058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.138383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.931446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.403223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.268524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.061007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.833191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.678339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.472398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.216103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.990433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.985063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.785002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.523980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.279181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.194771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.982172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.476987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.325085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.115544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.889566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.738017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.533715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.271372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.055055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.043025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.840481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.576792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.348403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.248065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.028776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.531624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.380461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.168957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.938721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.792940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.584960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.324168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.112875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.097150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.890148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.630800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.407017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.300032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.080782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.594198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.442645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.227204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.995172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.849880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.642406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.381879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.172371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.165773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.946433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.686555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.475147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.355121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.126264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.716664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.496076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.279026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.044052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.903051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.689862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.432397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.265873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.216427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.995248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.737575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.530634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.403938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.177144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.773017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.557839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.334099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.098776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.959881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.743560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.487868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.324759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.271778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.049981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.791846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.607784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.457349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.230797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.833637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.618541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.396445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.157301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.022571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.799626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.546466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.384673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.329332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.107618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.850202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.669237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.515265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.279975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.887694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.677660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.452666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.210884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.078502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.852299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.601811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.441881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.383448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.160842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.903297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.725357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.567563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.330248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.939884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.731209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.507995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.262361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.131716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.902145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.653549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.495596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.434972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.210434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.954587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.779009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.617745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.379964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:57.992397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.784743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.559998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.314639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.185700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.950853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.706945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.657674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.489363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.260772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.003412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.833250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.672960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.433327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.050123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.842920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.617864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.369444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.243071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.004032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.766024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.716135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.560244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.315872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.058596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.888401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.729939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:08.484823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.104297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:58.896318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:29:59.669925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:00.421580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:01.302178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.054511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:02.818967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:03.772757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:04.625068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:05.366647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:06.109834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.035155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-19T15:30:07.780098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-19T15:30:14.818855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
GitHub Repository IDGitHub StarsGitHub ForksGitHub SubscribersGitHub Open IssuesGitHub ContributorsGitHub Network CountRepository Size (KB)Duration Created to Now in YearsGitHub Stars (Log Scale)total lines of GitHub detected codetotal lines of GitHub detected code (Log Scale)GitHub Forks (Log Scale)GitHub Open Issues (Log Scale)GitHub License TypeGitHub Repo ArchivedcategoryPrimary language
GitHub Repository ID1.000-0.365-0.415-0.447-0.305-0.248-0.415-0.140-1.000-0.3630.0140.014-0.415-0.3100.0870.0770.2140.101
GitHub Stars-0.3651.0000.8190.6440.6850.5360.8170.3980.3651.0000.2490.2490.8570.6950.0000.0000.4040.134
GitHub Forks-0.4150.8191.0000.6280.6630.5530.9990.3820.4150.8200.2470.2471.0000.6880.0000.0000.3480.000
GitHub Subscribers-0.4470.6440.6281.0000.5890.5590.6250.3500.4470.6420.2280.2280.6510.5990.0000.0000.3480.079
GitHub Open Issues-0.3050.6850.6630.5891.0000.5480.6610.2840.3050.6840.1760.1760.6881.0000.0000.0000.3610.000
GitHub Contributors-0.2480.5360.5530.5590.5481.0000.5530.3930.2480.5350.2690.2690.5540.6210.0000.0000.4870.000
GitHub Network Count-0.4150.8170.9990.6250.6610.5531.0000.3820.4150.8200.2470.2470.9960.6880.0000.0000.3480.000
Repository Size (KB)-0.1400.3980.3820.3500.2840.3930.3821.0000.1400.3940.5340.5340.2870.2990.0000.0000.0460.285
Duration Created to Now in Years-1.0000.3650.4150.4470.3050.2480.4150.1401.0000.363-0.014-0.0140.4150.3100.1040.1010.2970.188
GitHub Stars (Log Scale)-0.3631.0000.8200.6420.6840.5350.8200.3940.3631.0000.2390.2390.8550.6940.0800.0000.5260.130
total lines of GitHub detected code0.0140.2490.2470.2280.1760.2690.2470.534-0.0140.2391.0001.0000.1880.2620.0000.0000.0000.558
total lines of GitHub detected code (Log Scale)0.0140.2490.2470.2280.1760.2690.2470.534-0.0140.2391.0001.0000.1880.2620.0800.1140.0810.375
GitHub Forks (Log Scale)-0.4150.8571.0000.6510.6880.5540.9960.2870.4150.8550.1880.1881.0000.6890.0880.0000.5260.115
GitHub Open Issues (Log Scale)-0.3100.6950.6880.5991.0000.6210.6880.2990.3100.6940.2620.2620.6891.0000.0000.0190.4570.039
GitHub License Type0.0870.0000.0000.0000.0000.0000.0000.0000.1040.0800.0000.0800.0880.0001.0000.0000.2290.181
GitHub Repo Archived0.0770.0000.0000.0000.0000.0000.0000.0000.1010.0000.0000.1140.0000.0190.0001.0000.0000.000
category0.2140.4040.3480.3480.3610.4870.3480.0460.2970.5260.0000.0810.5260.4570.2290.0001.0000.103
Primary language0.1010.1340.0000.0790.0000.0000.0000.2850.1880.1300.5580.3750.1150.0390.1810.0000.1031.000

Missing values

2023-10-19T15:30:08.582587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-19T15:30:08.813509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-19T15:30:09.039854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Project NameGitHub Repository IDProject HomepageProject Repo URLProject Landscape CategoryGitHub StarsGitHub ForksGitHub SubscribersGitHub Open IssuesGitHub ContributorsGitHub License TypeGitHub DescriptionGitHub TopicsGitHub OrganizationGitHub Network CountGitHub Detected LanguagesDate CreatedDate Most Recent CommitDuration Created to Most Recent CommitDuration Created to NowDuration Most Recent Commit to NowRepository Size (KB)GitHub Repo ArchivedDuration Created to Now in YearsNegative Duration Most Recent Commit to NowcategoryGitHub Stars (Log Scale)total lines of GitHub detected codetotal lines of GitHub detected code (Log Scale)GitHub Forks (Log Scale)GitHub Open Issues (Log Scale)Primary language
0pandas858127https://pandas.pydata.orghttps://github.com/pandas-dev/pandas[cytomining-ecosystem-relevant-open-source]400311681011213656411BSD-3-ClauseFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more[alignment, data-analysis, data-science, flexible, pandas, python]pandas-dev16810{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 386224.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 6804.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1277513.0, 'D': None, 'Dockerfile': 5751.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 457000.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': 10664.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 20324063.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 14392.0, 'Singularity': None, 'Smarty': 8486.0, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': 1196.0, 'eC': None, 'sed': None}2010-08-24 01:37:33+00:002023-10-16 16:52:20+00:004801 days 15:14:474801 days 15:45:14.5592630 days 00:30:27.559263334787False13.153425-1 days +23:29:32.440737relevant-open-source10.59740922492093.016.9286749.7297298.204125Python
1numpy908607https://numpy.orghttps://github.com/numpy/numpy[cytomining-ecosystem-relevant-open-source]2474186475952193435BSD-3-ClauseThe fundamental package for scientific computing with Python.[numpy, python]numpy8647{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 6220071.0, 'C#': None, 'C++': 205725.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 151476.0, 'D': 19.0, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': 3787.0, 'Fortran': 27683.0, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 1697.0, 'Mako': None, 'Mercury': None, 'Meson': 88875.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 10458450.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 17058.0, 'Singularity': None, 'Smarty': 4129.0, 'Stan': None, 'Standard ML': None, 'Starlark': 1842.0, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 5699.0}2010-09-13 23:02:39+00:002023-10-16 14:25:39+00:004780 days 15:23:004780 days 18:20:08.5592630 days 02:57:08.559263131902False13.095890-1 days +21:02:51.440737relevant-open-source10.11621717186511.016.6596359.0649687.693026Python
2arrow51905353https://arrow.apache.org/https://github.com/apache/arrow[cytomining-ecosystem-relevant-open-source]1261430963513908367Apache-2.0Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing[arrow]apache3096{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': 3709.0, 'Batchfile': 32824.0, 'C': 1507496.0, 'C#': 1525079.0, 'C++': 26864997.0, 'CMake': 732440.0, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1708430.0, 'D': None, 'Dockerfile': 135006.0, 'Emacs Lisp': 1064.0, 'Forth': None, 'Fortran': None, 'FreeMarker': 2312.0, 'Gnuplot': None, 'Go': 5619807.0, 'Groovy': None, 'HCL': None, 'HTML': 5604.0, 'Hack': None, 'ImageJ Macro': None, 'Java': 7353737.0, 'JavaScript': 128685.0, 'Jinja': 21888.0, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': 8771.0, 'M': None, 'M4': None, 'MATLAB': 722935.0, 'Makefile': 32659.0, 'Mako': None, 'Mercury': None, 'Meson': 62865.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': 11472.0, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 3288183.0, 'QMake': None, 'R': 1698163.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': 1794908.0, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 411936.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 461913.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 34537.0, 'TypeScript': 1108325.0, 'VBScript': None, 'Vala': 24798.0, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 1256.0}2016-02-17 08:00:23+00:002023-10-16 17:18:03+00:002798 days 09:17:402798 days 09:22:24.5592630 days 00:04:44.559263171101False7.665753-1 days +23:55:15.440737relevant-open-source9.44256355305799.017.8283888.0378668.270781C++
3duckdb138754790http://www.duckdb.orghttps://github.com/duckdb/duckdb[cytomining-ecosystem-relevant-open-source]123931157157309253MITDuckDB is an in-process SQL OLAP Database Management System[analytics, database, embedded-database, olap, sql]duckdb1157{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 1761733.0, 'C#': None, 'C++': 33576510.0, 'CMake': 146234.0, 'CSS': 182.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 343518.0, 'JavaScript': 12990.0, 'Jinja': None, 'Julia': 250635.0, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 14649.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1407442.0, 'QMake': None, 'R': 1693.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 26187.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 282562.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-06-26 15:04:45+00:002023-10-16 14:58:09+00:001937 days 23:53:241938 days 02:18:02.5592630 days 02:24:38.559263227662False5.309589-1 days +21:35:21.440737relevant-open-source9.42488737824335.017.4484637.0535865.733341C++
4parquet-mr20675636https://github.com/apache/parquet-mr[cytomining-ecosystem-relevant-open-source]2179133295130190Apache-2.0Apache Parquet[big-data, java, parquet]apache1332{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 5923431.0, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 14771.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': 8436.0, 'Scheme': None, 'Scilab': None, 'Shell': 14860.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 10354.0, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2014-06-10 07:00:07+00:002023-10-16 15:19:17+00:003415 days 08:19:103415 days 10:22:40.5592630 days 02:03:30.55926318475False9.356164-1 days +21:56:29.440737relevant-open-source7.6866215971852.015.6025687.1944374.867534Java
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1115Data-Analysis-AMATH-482245942374Nonehttps://github.com/priyanshir/Data-Analysis-AMATH-482[related-tools-github-query-result]00101NoneExploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 703573.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 33375.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-03-09 04:17:02+00:002020-03-14 22:41:50+00:005 days 18:24:481316 days 13:05:45.5592631310 days 18:40:57.5592632832False3.605479-1311 days +05:19:02.440737github-query-resultNaN736948.013.510273NaNNaNJupyter Notebook
1116Aging-Cell-Morphology-Cell-transformations-and-image-processing235024426Nonehttps://github.com/sumisingh/Aging-Cell-Morphology-Cell-transformations-and-image-processing[related-tools-github-query-result]00101NoneIdentifying cellular transformations associated with aging using image processing[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 8130261.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-01-20 05:18:57+00:002020-01-20 05:29:56+00:000 days 00:10:591365 days 12:03:50.5592631365 days 11:52:51.55926327874False3.739726-1366 days +12:07:08.440737github-query-resultNaN8130261.015.911104NaNNaNJupyter Notebook
1117CellMorphology148259959https://github.com/KnightofDawn/CellMorphology[related-tools-github-query-result]00101NonePython code to identify/analyze cells from microscopic stack images.[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 65906.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-09-11 04:25:09+00:002018-09-10 20:41:28+00:00-1 days +16:16:191861 days 12:57:38.5592631861 days 20:41:19.559263161False5.098630-1862 days +03:18:40.440737github-query-resultNaN65906.011.095985NaNNaNPython
1118ImagingCells137526283Nonehttps://github.com/jesnyder/ImagingCells[related-tools-github-query-result]00101NoneScripts to analyze cell number and morphologies using images.[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 1523.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-06-15 19:50:00+00:002018-08-31 19:21:33+00:0076 days 23:31:331948 days 21:32:47.5592631871 days 22:01:14.5592631False5.336986-1872 days +01:58:45.440737github-query-resultNaN1523.07.328437NaNNaNJupyter Notebook
1119course-bia119301640Nonehttps://github.com/denzf/course-bia[related-tools-github-query-result]00101MITCode examples for the course of Biological Image Analysis[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 6010.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-01-28 21:58:13+00:002018-01-24 03:22:19+00:00-5 days +05:24:062086 days 19:24:34.5592632091 days 14:00:28.559263203False5.715068-2092 days +09:59:31.440737github-query-resultNaN6010.08.701180NaNNaNPython
1120Cell-virulence-Detection-using-Image-Processing163268436Nonehttps://github.com/arushigupta148/Cell-virulence-Detection-using-Image-Processing[related-tools-github-query-result]00001NoneDesigned an automated tool to find the thickness of multiple cell capsules from images using morphological operations to generate plots of cell size vs capsular thickness, simplifying detection of virulence in yeast cells for mycologists[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 12989.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-12-27 08:27:06+00:002019-05-12 23:09:02+00:00136 days 14:41:561754 days 08:55:41.5592631617 days 18:13:45.5592631751False4.805479-1618 days +05:46:14.440737github-query-resultNaN12989.09.471858NaNNaNPython
1121Image-analysis152904377https://github.com/dguin/Image-analysis[related-tools-github-query-result]00001NoneThe repository contains code to analyze videos where each frame is a snapshot of the cellular status as a function of time. The program includes subroutines for segmentation protocols to pick a cell and differentiate it from the background when the signal to noise is low. The protocol docx explains what each code does and explains the order in which they must be run. As is the program analyzes FRET data from a cell, where the temperature increases as a function of time and one can evaluate the changes in the cell morphology as the cell is under heat stress[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 40670.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-10-13 18:53:42+00:002018-10-13 19:32:22+00:000 days 00:38:401828 days 22:29:05.5592631828 days 21:50:25.55926326False5.008219-1829 days +02:09:34.440737github-query-resultNaN40670.010.613246NaNNaNMATLAB
1122oct-image-analysis125785309https://github.com/ricster101/oct-image-analysis[related-tools-github-query-result]00001NoneWork developed with Adriana Costa during the course of Computer Vision and Biological Perception aiming to discover differences in mice retina[]None0{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 22755.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-03-19 01:29:32+00:002018-03-19 03:31:25+00:000 days 02:01:532037 days 15:53:15.5592632037 days 13:51:22.55926316False5.580822-2038 days +10:08:37.440737github-query-resultNaN22755.010.032540NaNNaNMATLAB